36414 >> Karin Strauss: Good morning, everyone. Welcome. ... Strauss, and I'm a researcher here at Microsoft Research. ...

>> Karin Strauss: Good morning, everyone. Welcome. My name is Karin
Strauss, and I'm a researcher here at Microsoft Research. And today
it's my pleasure to introduce Dr. John Cumbers. John is the founder of
SynBioBeta, which is a community of people and companies, start-up
companies around synthetic biology. John also has a pretty interesting
background. He has an undergrad in computer science, masters in
bioinformatics and Ph.D. in molecular biology from Brown University.
And John today is going to talk about the scene in the synthetic
biology industry. So welcome, John.
>> John Cumbers: Thank you. Hi, everyone. Thanks for having me.
Karin, thanks for inviting me. It was a short flight up from Mountain
View this morning. I live in the heart of Silicon Valley. And I've
worked at NASA for the last seven years. That's what brought me to
Silicon Valley at NASA Ames, right next to Microsoft actually in
Silicon Valley. But after living in Silicon Valley for a few years,
the start-up culture rubs off on you.
So I started a start-up myself. And like most startups, it failed
pretty quickly. But so I went back to NASA, but I felt so restricted
after working in a start-up, all the adrenalin rushing through your
blood being able to do whatever you want, hire whoever you want, buy
whatever you want, and then back to NASA where you have to fill out
forms to hire people, buy things or to use your credit card even.
So I went back to NASA but I only went back four days a week. On
Fridays I started what is now SynBioBeta. And SynBioBeta is an
activity hub for synthetic biology, start-up companies, investors,
thought leaders, and as the industry has matured, and I'll show you how
it's maturing with some charts shortly, it's now a community for the
whole synthetic biology industry. And we run conferences. That's what
we started off doing. And we had one just last week in San Francisco.
And particularly the theme of this conference was where tech meets
biotech. And so you'll probably recognize some of the speakers from
the tech world on the slide behind me, people like Tim O'Reilly was one
of the keynote speakers. Sam Altman, the president of Y Combinator,
along with some of the biggest names in the synthetic biology industry.
R. Kirk, CEO of Intrexon, the biggest synthetic biology company out
there with a market cap of four and a half billion dollars.
And Jason Kelly, one of the new startups in the field, called Ginkgo
Bioworks, which recently raised $45 million in a Series A.
So we bring together the community a couple of times a year, once in
San Francisco, once in London. That's how we started as a conference.
It's now my full-time gig. I left NASA just last month. And we also
do courses, training and we run a company database keeping track of who
is investing in what, who is doing what, and how the industry is
So I'm going to talk today about what synthetic biology is, give you a
quick introduction, talk about some of the trends in the industry that
we've been tracking and that we see. And then talk about some of the
companies that are in the industry and what they're doing. Before I do
that, I don't know who's in the audience, so could you put your hand up
if you could tell me -- I'm not going to ask you to do it, I just want
to know whether you could tell me how a gene relates to a protein. Put
your hand up if you have a good understanding of how a gene relates to
a protein. That's a good 50/50 mix. Do we know how many people we've
got online? Okay. If anyone online has a question or if any of you
have a question throughout please stop and ask, because as a former
computer scientist, I know the pain of the jargon that we have in
biology and how complicated it is.
I didn't know how a gene related to a protein 11 years ago when I made
the switch from computer science into bioinformatics then into a Ph.D.
in cell molecular biology. So what is synthetic biology? Well, you
could argue that synthetic DNA is a big part of synthetic biology.
This is the birth of Genentech. Now a multi-billion-dollar company
based in south San Francisco, the birth place of biotechnology. And on
the blackboard behind the two founders of Genentech you can see a few
things that are critical to the industry. One is -- I don't need the
pointer anyway.
>>: It's working.
>> John Cumbers: It is. Okay. Great. I'm just not pressing hard
enough. So you can see synthetic DNA up on the chalkboard here. You
can see a plasmid, which is a circular piece of DNA. And you can see a
E. Coli cell up here. And these three things combined into this
plasmid here.
And this is now the birth of biotechnology here. It's taking a cell, a
bacterial chassis, capable of producing things, and taking a gene of
interest, such as this piece of DNA, which encodes for something
interesting, and it's putting it on to another piece of DNA and putting
it inside a cell. So this is really genetic engineering. This is what
we've been doing for the last 30 some years is taking pieces of DNA
that exist in nature, putting them into a plasmid and booting that
plasmid up into a cell. And once you've done that, you can then start
to use technology such as fermentation, which we've been making alcohol
and wine and other enzymes or products and then growing up that
organism and producing whatever protein is made from that DNA.
And this was the first thing that Genentech produced, it was a small
peptide, a small protein of amino acids linked together, and it was
insulin. And so in the past diabetics would get their insulin from
slaughtering pigs, taking the pancreas from pigs and cows and
slaughtering it and using it as human insulin. Now in the future you
have Humulin, human insulin, which is recombinant, and so it takes the
DNA directly from your own insulin or human insulin and it slices it,
splices it into a plasmid, and boosts up the plasmid so you can brew
drugs just like you might brew alcohol and it's the small peptide
Hugely successful. The birth of biotechnology. And now a
multi-billion-dollar industry that we have. But that's not all biology
can make. What can we produce with these things? Chris, I'm sure you
>>: Hops.
>> John Cumbers: Yes, it's hops, barley, water, and there's one thing
that's missing in the production of beer and that's the yeast. And
that's not really put on the label but it's the most important thing.
And there's a wonderful place in San Diego called White Labs. And if
you're a home brewer, you probably know White Labs. They sell many
kinds of yeast. If you go to the tasting room at White Labs, you can
order a flight of beer, but there's something different about that
flight of beer. It's exactly the same ingredient that goes into every
beer, but they just change the yeast that's used to do the fermentation
and to make the alcohol. And it's five completely different tasting
beers demonstrating that the power of biology for making things, you
can use exactly the same inputs and get five different outputs based on
the strain of yeast. And so you can also make these wonderful flavors
such as cheese. And you can also make food. Does anyone know or does
anyone want to guess what this might be growing in Hawaii? It's
Spirulina. If you've seen it in the health food store, it's
spiral-shaped bacteria, complete protein source. You can buy it and
it's grown in large pools in Hawaii, but it's actually single cell
bacteria rather than any crops. This is Romanesque broccoli, something
that looks absolutely beautiful. It tastes great. And it just shows
the power of biology as a manufacturing technology, as does bread. You
would think that there's this amazing property of using yeast in bread,
something to do with the flavor or the enzymes that it produces, but
it's actually not. You use yeast in bread because it provides gas
inside as you're cooking and it has this then wonderful spongy texture.
It's not to do with the taste or the flavor but actually the texture.
And if you look around you on the beach, you see these wonderful
things, not just soft biological gooey, green gooey things, but these
amazing shells you can see for building structure.
And if you go into a redwood forest, you can also see these phenomenal
structures that are over 200 years old and dwarf in comparison to the
person sitting next to them. And this is an example of an engineered
system that was built out of mushroom bricks for the Museum of Modern
Art. And not just structural things, but also things that smell good
like this rose, and there's one of the companies Ginkgo Bioworks is
producing this rose-scented perfume. So all this is to try to
demonstrate to you the power of biology, versatile manufacturing
technology for producing many different kinds of products. But above
all, it's manufacturing gene technology. So how genes relate to
proteins, the first question I asked you, you have A's, C's, T's and
G's on a double helix of DNA and through this wonderful mechanism of
transcription and translation, which I'll show you a video of in a
second, you have these amazing structures such as these proteins
produced on the left-hand side. And many of the things that you see
around in nature or that you eat or that do things are proteins. And
proteins are replicated inside of cells, which themselves are
self-replicating. A single cell can divide in 20 minutes, if it's a
bacterial cell, or a bit longer if it's a mammalian cell. You have all
the self-replicating machinery going on for the production of cells and
then for the production of proteins and enzymes that are made from
those cells.
And they're all packaged up inside a nucleus, if it's a mammalian cell,
into your chromosomes which you're familiar with in terms of the 23
pairs you have. All this information packaged up for the production of
And this is what's actually going on inside your cells. This is a RNA
polymerase molecule. This is running along a piece of DNA, and it's
actually reading the code. And what we're about to see here is the
conversion of DNA into RNA and into protein. And that's called the
central dogma of molecular biology. And then any second now the
machinery is going to bind on -- this is the RNA polymerase
machinery -- it's going to start whizzing along, reading it, and this
is an animation but it's in real time. And you can see the ability of
this enzyme to run along the DNA. There it goes. Producing just like
a ticker tape coming out the other side this strand of RNA and these
yellow molecules that are flying around, these are also A's, C's, T's
and G's, with some exceptions for RNA. But, in general, you can now
see why I'm saying this is a manufacturing technology. Because you can
see this ticker tape coming out of the back. The DNA is being read.
And that's in the red color. And RNA is being produced and that's in
the yellow color. And so all this has been discovered over the last 20
years or so, is what this machinery is, how it works, and how we can
use engineered biology as this manufacturing base for the production of
many different things such as I showed in the previous slide. And the
two technologies which I think are really important for synthetic
biologists, one is in the reading of DNA, that is, the sequencing of
DNA. You can sequence your genome for about a thousand dollars now.
It cost about a billion dollars or more 10 years ago. And then the
storage of that sequence information in a computational form or in a
data form, and then the synthesis part, which is then the writing of
DNA, which allows us to write anything that we want to write and put it
back inside a cell. So what is synthetic biology? Synthetic biology
is trying to make biology easy to engineer. So it's not necessarily a
core technology. It's definitely those two technologies of reading and
writing DNA, but fundamentally it's a community of people who want to
see a very different biological future than the one that we have now.
And it's a bunch of engineers coming into the field of biology and
trying to understand how we can engineer biology. I think it's a
little akin to Web 1.0 and Web 2.0. When Web 2.0 came about, there was
a huge flurry of activity, a lot of excitement, a new generation of
people coming into the field, a new generation of investors backing
companies and startups in this field and a new generation of products
that we see now such as Flickr and Twitter and Facebook. But there was
also a bunch of people who said, hey, what is this Web 2.0; there's
nothing to it. We've been doing that since the beginning. You get
similar thing with synthetic biology. It's built on the foundations of
genetic engineering, but there's a number of people who say there's no
really new technology, it's kind of built on what we've been doing all
along. I'll show you some of those technologies and the dates of those
technologies which aren't really new. So I think there's some truth to
that. But there's a wonderful quote which was in Nature Biotechnology
a few years ago which I think sums up the field very well. I'm not
going to read it, but it talks about the principles of engineering and
the difference between science and engineering. And a lot of
scientific principles are underlying synthetic biology, but it's really
the engineering and the engineers coming into the field of biology
which are now trying to make advances in our ability to make things.
And so the quote talks about the Wright Brothers and how they didn't
invent flight. They didn't invent an airplane by understanding the
principles of aerodynamics. Instead, they understood enough to fly a
plane and to have a short repeatability cycle, an engineering cycle, so
they could fly many planes and learn from all the ones that crashed.
After a few years, they suddenly had a plane that could fly. But only
years later did scientists understand the principles of aerodynamics.
They understood how the plane flew. And I think we're in a similar
position with biology. We've got a lot of scientific underpinning that
can understand the principles of molecular biology. But we're really
going to make great advances when we try to design things with biology.
And build things with biology and later on the science will catch up
with the things that we've managed to build. And my favorite quote,
which I think predicts some of the trends coming, by Craig Venter, one
of the pioneers in the field, who says over the next 20 years synthetic
genomics will become the standard for making anything.
So I'm going to move on to some of the trends that we see coming, but I
want to pause in case there's any questions on anything I said so far.
Okay. Great.
So if you look back at the core technologies that I talked about, the
discovery of DNA, 1953, that it is a double helix. The first string of
synthetic nucleotides A's, C's, T's and G's that were strung together
in 1972 and the first reading of DNA, the first acceptance of reading
DNA, Sanger sequencing in 1975. Are you debating my date?
>>: No, the DNA on the far right is left-handed.
>> John Cumbers: That's a big faux paus. He said I have a left-handed
twist instead of a right-handed twist. I'll fix that.
>>: PhotoShop.
>> John Cumbers: I'll go back to the person who did it for me. So I
think that we're at the start of the fifth industrial revolution. So
we've gone through water and steam. Electricity. Automation in terms
of IT systems. I think worry currently in Internet of Things and the
cloud revolution, cloud computing. And I think the next one is the
bioindustrial revolution through synthesis and sequencing and the
predictable engineering of biology.
And this chart is taken from Rob Carson, who I'm sure if you don't know
you should get to know him, at the back of the room. The economists
call Rob's data the Carson curve, the equivalent of Moore's Law for
biology, which shows -- obviously you're familiar with Moore's Law -the doubling of the number of micro, of chips on -- sorry, of
transistors on a microprocessor.
Rob started to see this prediction in the falling costs of DNA
sequencing and the falling cost of DNA synthesis. So you can see,
reading DNA is on the red line there and writing DNA is on the green
line, and we've actually dropped faster than Moore's Law at about this
rate, because this is a log scale, and we were dropping faster than
Moore's Law here in the red. And it's becoming rapidly cheaper to
synthesize DNA, that is, to write DNA. And it's already vastly cheaper
to read DNA, that is, to sequence it.
>>: [indiscernible].
>> John Cumbers: It flattens because it's practically, it's extremely
cheap at the moment. So it wouldn't -- I mean it's already extremely
cheap. So could it get much cheaper, Rob?
>>: In principle, there's another point on there that's an order of
magnitude lower. Maybe it's picking up again. It's not just about the
technology it's about the economics of the markets. And Illumina
caused basically that crash in the price starting in 2004 with new
technologies. And then now they dominate the market and they have no
reason to lower the price. So it's not as if -- it's not as simple a
story that it might be with chips even though that story is more
complex. This is John's talk, so I'll shut up.
>> John Cumbers: I'll repeat for people listening on line. Rob's
saying Illumina came in here brought the price down rapidly, a little
competition incentive to bring the price down further at the moment.
So it could go down further. And I don't know if anybody saw this
paper, it was by Charles, Charlie Cockell's lab at Edinburgh
University. It came out in June. And I found it randomly through
Google search for something else. I really thought it was wonderful.
It was calculating the number of base pairs of DNA on the planet. And
they calculated 5.3 times 10 to the 31 million bases of DNA on the
planet. And then they came up with this operations per second on
nucleotide operations and compared it to the number of supercomputers
that it would be today if you treated the whole planet and the number
of DNA bases as a computational problem. Or as an operating system,
showing the equivalent of 10 to the 21 supercomputers. So it's a
wonderful paper. And I think if you start to take a step back and look
at this ecosystem of DNA and everything is programming and chugging
along, just as that video that I showed you, that's for a single gene
in a single cell. And my body has 100 trillion cells. And each of
those hundred trillion cells has 20,000 genes. And each gene could be
transcribed, there could be -- well, there are multiple copies. So
just inside me at the moment is an incredible biological computing
platform, not my brain, but just the DNA itself. So it's a fantastic
paper. And maybe that's somebody you would want to invite to talk to
your group in the future.
>>: That's sort of the hitchhiker's guide to the galaxy hypothesis of
the meaning of the earth: The earth is a giant supercomputer designed
to answer the question. Okay. You know where I'm going.
>> John Cumbers: So the answer is 42.
>>: The answer is 42. What is the question?
>> John Cumbers: Exactly. [chuckling]. And so now I'm going to talk
a little bit about some of the funding trends and what's going on in
the industry. And this is actually, before we dive into the industry,
this is federally funded research for synthetic biology. Between '08
and 2014, it's about $820 million. It's been an initiative of the U.S.
government, synthetic biology in terms of funding the engineering of
biology, and that's because it's an extremely important area of the
economy. This is also some of Rob's data that he gave to a Senate
briefing in 2013 looking at this is 2012 data, the total revenue from
genetically modified systems is $353 billion in the U.S.
And a nice
comparison is if you compare it with the worldwide semiconductor
revenue for 2012, which is about 303 billion. So it just shows you the
power of genetic engineering and genetic modification. And here it's
roughly split between crops, industrial, such as chemicals materials
and biologics, so drugs. Then if you look at a couple of other trends,
this is iGEM, Internationally Genetically Engineered Machines
competition. This is the first robotics for synthetic biology. If
you're familiar with the robotics competition, this is the equivalent
for genetic engineering. And it's phenomenal. And the reason I put
this slide in is because Microsoft funded this -- back in 2003,
Microsoft gave MIT a whole bunch of money for interesting projects.
And one of the projects that was funded out of that was the iGEM
competition. It wasn't called iGEM at the time. But Microsoft dabbled
in synthetic biology for a few years funding various things. I was at
a conference here. In fact, Chris, did we meet then in ->>: Might have been.
>> John Cumbers: Back in 2009 at the University of Washington. 2008.
About standards for synthetic biology. That's where the synthetic
biology language came from. SBOL. And a number of other things. And
when you look at the iGEM teams worldwide, there's over 250 of them. A
quarter of them are in China. And a huge growth in the number of teams
year on year. It's a lot of fun. There's a UW team that does very
well every year. You look at the academic publications, it's growing
exponential in terms of the number of people publishing about synthetic
biology. And when you look at the number of companies in the industry,
that's also growing, and you can see there's over 200 companies that
self-identify as synthetic biology companies, growing about 20, 25 a
year. And we track all this in a company database. In 2015 alone,
there's been over half a billion dollars invested from venture money in
synthetic biology companies. And that number of companies is growing,
and the amount that's being funded, being put into those companies, is
also growing. Y Combinator got interested in this. This is the early
stage seed funding, based in Silicon Valley. 20 percent of their
companies are now biotech companies. 10 percent synthetic biology
companies. There's Indie Bio, another accelerator in San Francisco,
and a branch they have in Cork, Ireland as well. What's notable a lot
of the new money coming into these companies, a big portion, half a
billion dollars, is coming from tech entrepreneurs. So Bill Gates
invested in the editor medicine round. Tim O'Reilly, as I talked about
earlier, has been investing in Riffyn. Y Combinator funded a whole
number of these. And Eric Schmidt through his Innovation Endeavors
Fund and some of the PayPal founders such as Peter Thiel, but also you
see PayPal, Yahoo! and Netscape and Twitter. A lot of these are tech
companies now getting into biotech.
>>: Where do you draw the line between biotech and synthetic biology?
>> John Cumbers: It's exactly, I mean, so the question was where do I
draw the line between biotech and synthetic biology. And it's about
the same line that I draw between Web 1.0 and 2.0 and I think it's
difficult to define that's why I made a point that the companies
self-identify synthetic biology companies, because I see it more of a
movement, for people who want to rationally engineer biology and make
it predictable. And a lot of people in biotechnology are more focused
on the application rather than the process that they had gone through.
So it's a good question, and I'm kind of dodging it. But I'm going to
give you some examples of the companies that are working.
And this is something we call the synthetic biology stack. And it's
just a grouping of the companies that are in the industry, and it's a
nice way to think about it.
I'm just going to pause. Any other questions before I dive into the
third part? So the synthetic biology stack starts at the base with
gene and genome synthesis, that is synthesizing A's, C's, T's and G's.
And there's a whole number of companies who are making synthetic DNA.
So there's a Blue Heron was a Seattle company. It was purchased by
OriGene, correct, and was OriGene purchased by someone recently or is
OriGene still -- is OriGene in Seattle. Blue Heron was one of the
first companies in synthetic DNA based in Seattle. But you can see
there's OriGene here. And a lot of these are in the Bay Area or
Massachusetts. So a large number of companies working in this area.
And it's fast becoming a commodity business to synthesize and make DNA.
And as I said earlier, the price is falling. The basic principle how
you can make oligo, small strings of A's, C's, T's and G's, chip-based
synthesis which synthesizes these on top of a substrate and assembly
technology which then aligns these sequences together into longer
strands. This is one of the new entrants to the market. Emily
Leproust from Twist. They've raised over $80 million. And a new
technology based on silicon rather than glass for synthesizing the DNA.
Now, Geneious, one of the companies from the UK, is making synthetic
DNA libraries that's very diverse strings of DNA which are used for
many different applications. And Synpromics, a Scottish company that
is making synthetic promoters, that is, promoters are the short pieces
of DNA at the beginning of a gene to turn it on or off. And they use
bioinformatics approaches where all of this sequence information that
we've been storing in databases for the last decade, they're now using
search algorithms to find the genes, find the area before the gene that
is a promoter that turns it on or off and then pulling them all out and
then making customized promoters so you can turn on and off different
genes at will.
>>: Is this for a specific type of organism? Or ->>: This is only for eukaryotic cells at the moment. So it doesn't
work in bacteria, this particular algorithm. And I don't know -- I
don't know any more details than that. But there are different -there's different machinery used for bacteria as for eukaryotes. DNA
2.0 down in Menlo Park, they do machine learning for protein
optimization. And I'll come back to machine learning algorithms in a
second. But also doing a lot of DNA synthesis themselves. Next as we
go up the stack, this will build on each other until we get to the
applications at the top, are the CAD tools for gene and genome design.
You can see a number of different companies -- there's a lot of
companies in this space because it's relatively cheap to get into
compared to doing wet lab biology. Just a few of these, Benchling,
recently funded by Andreessen Horowitz for doing genome editing
analysis in the cloud, very nice tool for doing that. Desktop
genetics, which has a platform for CRISPR genome editing. CRISPR,
you've probably read about in the mainstream press. It's a very
exciting technology that allows you to edit cells invivo; that is, I
don't need to go through the process of purifying DNA, editing DNA,
putting DNA back into a cell, replicating a cell. Instead, I can
literally inject -- inject CRISPR/Cas9 into my leg, if I wanted to do
that, and edit the cells directly in my leg. And there's been
experiments of being able to cure paralysis in rodent legs by injecting
this CRISPR into it. It was invented three years ago, or discovered
three years ago. It's a natural mechanism. And in the four years
we've been running the conference from having no CRISPR talks to about
50% CRISPR talks to now about probably 80% CRISPR talks. Everyone is
doing CRISPR. And it's extremely exciting to see us speeding up the
design build and test cycle in biology. And at Microsoft Research,
Andrew Phillips, who many of you will be familiar with, is looking at
modeling and coming up with programming language in tools for
simulating and analyzing models of complex biological systems. So more
systems biology than synthetic biology but definitely some great work
going on in the UK at Microsoft Research there. And Riffin is a new
company founded two years ago. And Riffin is a platform technology for
cloud analytics of lab data. So it feeds in a lot of different data
from the lab and has a platform for analyzing that. This is a fairly
new area. Cloud labs and automation. Cloud labs is just like Azure or
AWS or Google. I can't remember what Google's platform is called. But
whatever that company's platform is called. And this is real exciting
because the same thing is happening to biology. Instead of having
mainframe, instead of having expensive lab equipment in your own lab,
you can now rent out lab equipment in somebody else's lab. So there
are two companies that are doing that. Emerald Therapeutics and
Transcriptic, and they have automated labs. And you can use an API for
using those pieces of equipment, those pieces of equipment in their
lab. So you can send them samples. You can put it in their machines.
They will run your script that you've written and send you back either
the data or the sample. You don't need to own a lab. And we're
calling this virtual bio, the idea that you don't need an expensive lab
anymore or even a lab at all to have a biotech start-up.
>>: So, John, how general are these pipelines, depending on if you
have different experiments, they don't have the equipment, you can't do
it. So how complete and general are these platforms?
>> John Cumbers: Sure. So the question is how complete are the
platforms. I think it's pretty -- I mean, Transcriptic has -- I think
the answer is the forefront of biology, the forefront of science is
always bleeding edge. So at labs you're always going to get somebody
trying to come up with a new assay or new way of doing something or
inventing a new piece of equipment to solve the question that they're
trying to answer. So it's difficult to say how on a scale of how
complete they are. I think for all standard molecular biology
procedures, such as purifying DNA, extraction, growth, mixing and
analysis, I think 100 percent. Then when you start moving to new
things, they're constantly playing catch up, but I think it's pretty
good. One thing we're doing, I'll come under this in a second, is a
competition called Blue Sky Bio where we're giving out resources to
startups to have them come up with new ideas of startups that they can
do relying on technologies like this. And so for an academic pursuit,
it will be limited, because you're at the edge of the envelope in terms
of what you're trying to come up with, but for a start-up that you're
doing, you could view it as a different problem. I have these
resources available to me and what problem can I answer so that I don't
need a lab. So I think it's a different kind of experiment, different
kind of question you'd do. And Emerald Therapeutics more focusing on
the analytics side in terms of drug development and analysis.
>>: So for these kind of experiments, like how long do they typically
>> John Cumbers: How long would the experiment take? It depends. You
could do a bacterial growth curve looking at how fast bacteria grow in
just a few hours. If it's not -- if it's something faster than that,
let's say a light assay where you're looking, the production of light,
it could be just a few seconds. So or it could be if it's a
slow-growing bacteria, it could be weeks. And there are different
business models for how you pay for these products as well, whether
they're per hour or they're a subscription model. So I've got
automation companies on here as well. You've got companies that are
looking at automation of processes, lab site has this amazing piece of
equipment for liquid handling, for moving droplets of liquid around
using sound. So if you've been in a molecular biology lab anytime in
the last 100 years or 50 years, there's a lot of pipetting, a lot of
moving of liquid a lot of repetition. So you're hiring the smartest
people in the world to stand at a lab bench and concentrate on a
repetitive task for six years of a Ph.D., it's not the most productive
use of time. So I think this is a huge area for growth in terms of the
ability to automate a lot of this. And there's Eric Clavins at UW, is
it Aquarius.
>>: Aquarium.
>> John Cumbers: His company is well worth a visit. I haven't been
there but I hear it's really interesting. So a lot of these companies
are working on automation and cloud. So this is Transcriptic that I
just mentioned just now. And they've been funded about $14.5 million.
And really looking at lowering costs, increasing precision and allowing
you to automate a lot of these tasks. This is a really interesting
company in the UK allowing you to do tens of millions of cells at a
time and analyzing them using mass spec. So at the moment mass
spectometry is very expensive for looking at the atoms that are inside
a sample. This can do tens of millions of cells, vastly faster and
cheaper than anything that's out there. So then as we move up, more
towards the biology and less away from the tools, we see organism
engineering platforms. That means you've created an organism or a set
of organisms or a set of tools and you're able to use these organisms
for producing many different things. So there's a number of companies
in this area. Ginkgo Bio is one of the early companies, and this was
the first company that Y Combinator funded so they have a platform for
high throughput, automation of strain engineering. So if you want to
produce a yeast cell that has a particular smell characteristic because
you're producing a perfume, then you can give your strain to them and
they would ramp up the production of that particular molecule, or they
will design the organism for you if you have some new fragrance that
you want to design or a food product or a material product. Zymogen is
in a similar category to that. Zeda which is here in Seattle is doing
similar, using a big data platform and machine learning to optimize the
production of pharmaceuticals. Synthase is a company that's based in
the UK. They, instead of taking in the organism in-house, they will
sell a complete system end to end for a company that might want to have
an automated system for the production of whatever the company's
producing. And an they are is a new programming language that they've
produced for engineering of biology.
And this is Tim Fell, who is the CEO. Zymogen, who recently raised 44
million in a Series A, very similar business model related to Ginkgo I
mentioned a moment ago working in chemicals, pharmaceuticals and food.
And right at the top layer we have the application. So the line's kind
of blurred between the platform and applications because some companies
make both. But you can see a lot of the interesting applications, a
lot of the CRISPR companies that I just mentioned, that tool for invivo
editing, such as Intelia or [indiscernible], or CRISPR or Caribou. But
then also some of the more interesting applications are things like
bulk threads, which is a spider silk. This is Dan Widmaier, who is the
CEO. You might have seen that NorthFace jacket that just came out last
week made of spider silk. It's a competitor to these folks. They've
raised $40 million to date and they've taken the genes from the spider
and they've put them into bacteria or yeast and they are now able to
spin spider silk just like you might grow yeast for production of
alcohol, then are able to grow spider silk instead of alcohol. Or a
mixture. No, I'm joking. So really interesting performance properties
of these fabrics, because they're able to engineer the DNA and engineer
then the property of the material that they're producing. This is a
French company called Eligo Bioscience. They're actually doing
engineered probiotics. You've heard of probiotics in yogurt and things
to keep your gut in good shape. They're engineering those organisms
for particular health properties. Another French company, Thomas
Landren, is making the first renewable inks that is things for dying
materials, which is a very damaging process to the environment. The
whole process for producing inks and bio-based solutions can be much
more sustainable, nontoxic and recycleable as well. And Arzeda I
mentioned earlier, has a combination platform for metabolic engineering
and they're particularly working in drugs and pharmaceuticals. And
they're based here in Seattle. I wanted to touch on big data and
machine learning, because this was the session that Andrew Phillips
chaired at our conference just last week. And we had a giant
competition. This was a give-away I mentioned, this blue sky bio
competition. Microsoft was a sponsor of this competition. So we gave
out cloud computing Azure time to the winner of the competition, which
was Koliber Bioscience, and Koliber is based down in San Diego. And
you list is the founder and they're also making an engineered probiotic
strain of yogurt. So the idea is that one of the cures for depression
or one of the treatments for depression is triptophan, a particular
amino acid. But it's known to cure depression, but you have to eat a
huge amount of triptophan in order to do that. So his idea was to
produce a yogurt that had this probiotic strain in it to produce higher
levels of triptophan you could eat yogurt for breakfast and it would
have the probiotic. It's never been shown or gone through approval for
this kind of medicine where you're actually eating the organism that's
producing the drug, but I think we're going to see a lot more of it.
Because we have more bacterial cells in your gut than you do human
cells in your body by many times over. So we're starting to understand
and work with the organisms that are living with us rather than trying
to kill them all with antibiotics. And in particular she was using
these modeling tools and data analytics to try to find the proteins
that she could express or the circuit she could build for the
triptophan production. And she gave this wonderful talk where she
really outlined the problems in machine learning that you can solve
using synthetic biology, which I thought might be of interest to you
folks. How to optimize promoters in pathways, we talked about that,
how can we design these various things, either a promoter, which is
what the Scottish company Synpromics was doing, or Pathway, which is
doing for triptophan production. Trying to understand which genes are
essential or which can be used for fermentation on different scales.
And the errors that she thought was interesting for the future was for
gene discovery, understanding which hosts could can be used for the
production of different things, that is, which organism, maybe yeast or
bacteria or different kinds of yeast or bacteria, and which drugs could
be used for different patients. So she gave a really good talk
outlining the different options for that. So I want to just wrap up
and talk about a couple of events we've got coming up next year. One
we're doing a big event in London in the beginning of April. And we've
been doing an event in London for four years. And then our first event
in China will be in June 2016, really trying to understand what's going
on in China and organize a tour of synthetic biology companies there.
If you're interested in learning more, we do courses in synthetic
biology and we send out a weekly e-mail news digest about who is doing
what and who is getting funded and what products there are available.
And with that, I'll thank you all for listening. I'll take any
questions. [applause].
>> Karin Strauss: Any additional questions for John.
>>: I have a question. At one time sort of safety in the industry, I
remember the IDM conference a lot of controversy about sitting and
letting people loose with these tools. There was a big issue. Are
people still worried about it? Have we just sort of outgrown it at
this point. What is the current thinking?
>> John Cumbers: That's a good question about safety in the industry,
and I think the biggest fear was that if you give anybody access to
these tools, then anybody could create a biological weapon in their
basement or anybody could kill somebody and I think I gen's done a
really good job of building bio safety into the educational curriculum,
and I think it's a much more practical training in safety than you
might get just through following some regimented thing at a university.
So I think safety from that regard I think is it's certainly not
forgotten it's front and center of the competition still. And it's
taught in every undergraduate team.
I think in terms of the will people create nasty strains to create bio
weapons and things like that in their basements, I think potentially,
but with every technology there comes from an engineering point of view
a responsibility to do good with the technology and has the potential
to do bad with it as well. And you can cultivate anthrax from soil I'm
sure here in Seattle without needing to go to the extent of trying to
genetically engineer something and anthrax is pretty bad. So I think
with the knowledge that's out there you can already do pretty nasty
things. So I think what the field has done is to grow this pool of
responsible use in people who think smart about what they can do with
the technology and what they want to do with the technology.
>>: Questions? All right. Let's thank John again. [applause]